Finally, it is possible to allow the system to learn by interacting with the environment. However, many policy formulations that allow learning (e.g. artificial neural networks) have deficiencies which make their application in an industrial domain with the abovementioned requirements challenging. Primarily during the learning phase, dangerous behaviors can be produced and even state-of-the-art RL methods need hundreds of hours of interaction time [1]}. Learning in simulation can help to reduce downtime and dangers for the real system. But many policy formulations are black boxes for operators and it can be hard to predict their behavior, which could hinder the trust to the system [2]} Moreover, the simulation-to-reality gap [3]}, [4]} is bigger in lower-level control states (i.e. torques), and policies working directly on raw control states struggle to transfer learned behaviors to the real systems [1]}. Our policy formulation consisting of behavior trees (BT) with a motion generator [6]} has shown to be able to learn interpretable and robust behaviors [7]}.
